• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.48-52
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• 2008

In this paper, we propose a new selective subspace projection method in order to recognize the occlusive face image effectively. The conventional subspace projection method is project to basis image using a full image of face. The face recognition rate has reduced because the face characteristic is easy to be distorted by occlusion. To overcome this problem, the proposed method first decide to occlusion. If it hasn't an occlusion, we get the feature vectors with total basis projection using the conventional subspace projection method. If it has an occlusion, we get one with partial basis projection. We get better recognition rate than conventional PCA and NMF using AR face database with occlusive face images.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.2 s.302
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• pp.39-48
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• 2005

This paper proposes a novel approach to recognize nine kinds of motion for a multifunction myoelectric hand, acquiring four channel EMG signals from electrodes placed on the forearm. To analyze EMG with properties of nonstationary signal, time-frequency features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. From experimental results, we show that the proposed method enhances the recognition accuracy, and makes it possible to implement a real-time pattern recognition.

• Journal of Korea Multimedia Society
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• v.7 no.10
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• pp.1478-1484
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• 2004

This paper presents a method that controls facial expression in realtime of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. The space of expression is created from about 2400 frames of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. Facial expression of 3D avatar is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the Principal Component Analysis(PCA) projection. To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. This paper evaluates the results.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.645-652
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• 2004

Among the biometrics, signature shows more larger variation than the other biometrics such as fingerprint and iris. In order to overcome this problem, we propose a robust offline signature verification method based on PCA and LDA. Signature is projected to vertical and horizontal axes by new grid partition method. And then feature extraction and decision is performed by PCA and LDA. Experimental results show that the proposed offline signature verification has lower False Reject Rate(FRR) and False Acceptance Rate(FAR) which are 1.45% and 2.1%, respectively.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.97-104
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• 2009

This paper describes comparison and analysis of methodology which enables us in order to search the projection technique of optimum for projection in the plane. For this methodology, we applies the high-dimensional facial motion capture data respectively in linear and nonlinear projection techniques. The one core element of the methodology is to applies the high-dimensional facial expression data of frame unit in PCA where is a linear projection technique and Isomap, MDS, CCA, Sammon's Mapping and LLE where are a nonlinear projection techniques. And another is to find out the methodology which distributes in this low-dimensional space, and analyze the result last. For this goal, we calculate the distance between the high-dimensional facial expression frame data of existing. And we distribute it in two-dimensional plane space to maintain the distance relationship between the high-dimensional facial expression frame data of existing like that from the condition which applies linear and nonlinear projection techniques. When comparing the facial expression data which distribute in two-dimensional space and the data of existing, we find out the projection technique to maintain the relationship of distance between the frame data like that in condition of optimum. Finally, this paper compare linear and nonlinear projection techniques to projection high-dimensional facial expression data in low-dimensional space and analyze it. And we find out the projection technique of optimum from it.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.47-53
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• 2004

This paper proposes an algerian for determining robot location using appearance-based paradigm. First, this algorithm compresses the image set using Principal Component Analysis(PCA) to obtain a low-dimensional subspace, called the eigenspace, and it makes a manifold that represent a continuous-appearance function. Neural network is employed to estimate the location of the mobile robot from the coefficients of the eigenspace. Then, Kalman filtering scheme is used for the fine estimation of the robot location. The algorithm has been implemented and tested on a mobile robot system. It is shown that the robot location is estimated accurately in several trials.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2180-2188
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• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

• Journal of the Korea Computer Graphics Society
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• v.10 no.2
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• pp.51-56
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• 2004

모션 데이터들을 그래프로 저장하고 이를 모션합성에 이용하는 기존의 연구들은, 모든 모션 프레임간 연결비용계산으로 인하여 그래프 생성에 많은 시간이 걸린다는 단점이 있다. 본 논문에서는 이런 단점을 보완하여 빠르고 효과적으로 그래프를 생성하는 방법을 제시한다. 우선, PCA를 이용하여 모션들을 2차원에 투영시키고, 2차원 상의 간단한 거리계산으로 전이에지가 존재할 가능성이 큰 프레임 쌍들을 찾아낸다. 다음으로, 이런 프레임 쌍에 대해서만 연결비용을 계산하여 그래프를 생성한다. 따라서, 모든 프레임에 대한 비용계산에 비해 본 논문에서 제안한 방법은 효율적으로 그래프를 생성하게 된다.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.479-483
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• 2011

The automation of production process is actively expanding for the purpose of the cost reduction and quality assurance. Among these, automatic tracking of the product along the whole process of the production is also important topic. Typically this is done by adopting OCR technology. Conventional OCR technology operates well on the rather good quality of the image like as printed characters on the paper. In industrial application, IDs are marked on the metal surface, and this cause the height difference between background material and character. Illumination systems that guarantee an image with good quality may be a solution, but it is rather difficult to design such an illumination system. This paper proposes an algorithm for the recognition of vehicle's ID characters using edge projection and PCA (Principal Component Analysis). Proposed algorithm robustly operates under illumination change using the same parameters. Experimental results show the feasibility of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.67-76
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• 2008

Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.